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A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling

Author

Listed:
  • Vahid Nourani

    (University of Tabriz
    Near East University)

  • Amir Molajou

    (Iran University of Science and Technology)

  • Ali Davanlou Tajbakhsh

    (University of Tabriz)

  • Hessam Najafi

    (University of Tabriz)

Abstract

The suspended sediment load (SSL) modeling generated within a catchment is a significant issue in the environmental and water resources planning and management of watersheds. The estimation methods of SSL are limited by the important parameters and boundary conditions (which are based on the flow and sediment properties). In this situation, soft computing approaches have proven to be an efficient tool in modelling the sediment load of rivers. In this study, the hybrid Wavelet-M5 model was introduced to model SSL of two different rivers (Lighvanchai and Upper Rio Grande) at both daily and monthly scales. In this way, first, the runoff and suspended sediment load time series were decomposed using the wavelet transform to several sub-time series to handle the non-stationary of the runoff and sediment time series. Then, the obtained sub-series were applied to M5 model tree as inputs. The obtained results for the Upper Rio Grande River at daily time scale, showed the better performance of Wavelet-M5 model in comparison with individual Artificial Neural Network (ANN) and M5 models so that the obtained Nash-Sutcliffe efficiency (NSE) was 0.94 by the hybrid Wavelet-M5 model while it was calculated as 0.89 and 0.77 by Wavelet-ANN (WANN) and M5 tree models, respectively. Also, the obtained NSE for the Lighvanchai River at monthly time scale was 0.90 by the hybrid Wavelet-M5 model while it was calculated as 0.78 and 0.69 by Wavelet-ANN (WANN) and M5 tree models in the verification step, respectively.

Suggested Citation

  • Vahid Nourani & Amir Molajou & Ali Davanlou Tajbakhsh & Hessam Najafi, 2019. "A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1769-1784, March.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:5:d:10.1007_s11269-019-02216-9
    DOI: 10.1007/s11269-019-02216-9
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    References listed on IDEAS

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    1. Manish Goyal, 2014. "Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1991-2003, May.
    2. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    3. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "Erratum to: River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 2123-2123, May.
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    Cited by:

    1. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    2. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    3. Babak Vaheddoost & Hafzullah Aksoy, 2019. "Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3899-3911, September.
    4. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.
    5. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.

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    More about this item

    Keywords

    Suspended sediment load modeling; Runoff; Decision tree; M5 model tree; Wavelet transform; Artificial neural network;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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